countwords
| generate-random-numbers | countwords | |
|---|---|---|
| 1 | 43 | |
| 0 | 209 | |
| - | - | |
| 0.0 | 5.9 | |
| about 3 years ago | about 2 years ago | |
| Zig | Rust | |
| - | MIT License |
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generate-random-numbers
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Performance comparison: counting words in Python, Go, C++, C, Awk, Forth, Rust
Here is a similar excercise. Ad-hoc programs in different languages generate a long list of random numbers. How long does it take? https://github.com/posch/generate-random-numbers
countwords
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How fast is really ASP.NET Core?
"dang, I didn't know that was 50x faster than the idiomatic way" or "hey, I didn't know that this implementation in the stdlib prioritized this over that and made this so slow, that's interesting" -- .e.g, there's some kinda neat language details to be found in something like Ben Hoyt's community word count benchmarks repo and 'simple' vs 'optimal' code: https://github.com/benhoyt/countwords
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Correct name for word matching problem
It benchmarks programs that count the total number of unique words in some input. It's not exactly equivalent to your problem, but it's similarish. All of the programs used some kind of hash map for lookups, but I contributed a program that used a trie. Its performance in my experience varies depending on the CPU interestingly enough. On my old CPU (i7-6900K) it was a little slower, but on my new cpu (i9-12900KS) it was faster.
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Performance comparison: counting words in Python, C/C++, Awk, Rust, and more
Why not read the source code? :-)
I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...
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do you guys prefer functional programming style when using rust?
My own code example of a drastic speed up (~25%) simply replacing a couple of for loops with iters: https://github.com/benhoyt/countwords/pull/115
- Ripen scripting engine (Similar to RetroForth, but tiny)
- Performance comparison: counting words in Python, Go, C++, C, AWK, Forth, and Rust
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The difference between Go and Rust
And yet Go was faster than Rust in a simple app that count words: https://benhoyt.com/writings/count-words/
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How to Rapidly Improve at Any Programming Language
> but the performance profiles & characteristics that we must know about in order to make a choice on which tool to use. And it shouldn't be that each user has to figure it out on their own, dig into PR's or whatever.
That's an interesting take – I like the idea of a catalog of standard tasks with implementations in several languages as well as their performance characteristics. I suppose Rosetta Code gets the ball rolling with this, but it's missing some performance metrics. It reminds me of [Ben Hoyt's piece](https://benhoyt.com/writings/count-words/) on counting unique words in the KJV Bible in different languages.
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Faster string keyed maps in Go
This article shows that map lookups can be optimized by using the (unintuitive) pattern:
- Go beats out several top languages including Rust in this performance matchup
What are some alternatives?
word_frequency_nim - The word frequency program, written in simple nim.
CPython - The Python programming language
raikv - Persistent key value store, serverless shared memory caching
coreutils - upstream mirror
adix - An Adaptive Index Library for Nim
llfio - P1031 low level file i/o and filesystem library for the C++ standard
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
securitytxt.org - Static website for security.txt.
wyhash - The FASTEST QUALITY hash function, random number generators (PRNG) and hash map.
leocad - A CAD application for creating virtual LEGO models
abseil-cpp - Abseil Common Libraries (C++)
Klib - A standalone and lightweight C library